Distributing Services in Graph-Based Computations

ABSTRACT

A service request is processed according to a computation graph associated with the service by receiving inputs for the computation graph from a service client, providing the inputs to the computation graph as records of a data flow, receiving output from the computation graph, and providing the output to the service client. Data flows are processed concurrently in a graph-based computation by potentially concurrent execution of different types of requests, potentially concurrent execution of similar request types, and/or potentially concurrent execution of work elements within a request.

CLAIM OF PRIORITY

This application claims priority to Provisional Patent Application Ser. No. 60/836,745, filed on Aug. 10, 2006, the entire contents of which are hereby incorporated by reference.

TECHNICAL FIELD

This invention relates to distributing services in graph-based computations.

BACKGROUND

Complex business systems typically process data in multiple stages, with the results produced by one stage being fed into the next stage. The overall flow of information through such systems may be described in terms of a directed data flow graph, with vertices in the graph representing components (either data files or processes), and the links or “edges” in the graph indicating flows of data between components.

Graphs also can be used to invoke computations directly. The “CO>OPERATING SYSTEM®” with Graphical Development Environment (GDE) from Ab Initio Software Corporation, Lexington, Mass. embodies such a system. Graphs made in accordance with this system provide methods for getting information into and out of individual processes represented by graph components, for moving information between the processes, and for defining a running order for the processes. This system includes algorithms that choose interprocess communication methods and algorithms that schedule process execution, and also provides for monitoring of the execution of the graph.

A distributed services architecture allows computer programs to access capabilities of other programs through a network interface, such as the world wide web, without having to incorporate the functionalities of those programs into their own operating code.

SUMMARY

In general, in one aspect, a service request is processed according to a computation graph associated with the service by receiving inputs for the computation graph from a service client, providing the inputs to the computation graph as records of a data flow, receiving output from the computation graph, and providing the output to the service client.

Implementations may include one or more of the following features.

Receiving the inputs includes identifying the inputs within a service request from the service client, and providing the inputs to the computation graph includes associating a request identifier with the service request, providing a work element including the inputs to the computation graph, associating the request identifier with the work element, and communicating the association of the request identifier with the work element to an output node. Identifying the inputs includes extracting the inputs from an encapsulated request. The encapsulated request includes a SOAP request. Receiving the output includes receiving a work element including outputs from the computation graph, and identifying a request identifier associated with the work element, and providing the output to the service client includes generating a service response corresponding to the service request identified by the request identifier, the service response including the outputs included in the work element. Generating the service response includes embedding the outputs in an encapsulated response. The encapsulated response includes a SOAP response.

A second service request is processed according to the computation graph by receiving second inputs for the computation graph from a second service client, providing the second inputs to the computation graph as records of a second data flow, receiving second output from the computation graph, and providing the second output to the second service client; and in the computation graph, processing the first inputs and the second inputs concurrently. Processing the first inputs and the second inputs concurrently includes identifying a first subgraph applicable to the first inputs, processing the first inputs in the first subgraph, identifying a second subgraph applicable to the second inputs, processing the second inputs in the second subgraph, receiving first outputs from the first subgraph, and receiving second outputs from the second subgraph, in which the first subgraph and the second subgraph are executed simultaneously. Processing the first inputs and the second inputs concurrently includes identifying a first instance of a subgraph as available, processing the first inputs in the first instance of the subgraph, identifying a second instance of the subgraph as available, processing the second inputs in the second instance of the subgraph, receiving first outputs from the first instance, and receiving second outputs from the second instance, in which the first instance of the subgraph and the second instance of the subgraph are executed simultaneously.

In general, in one aspect, a data flow is processed in a graph-based computation by identifying a first graph applicable to a first record in the data flow, identifying a first subgraph applicable to a first work element in the first record, processing the first work element in the first subgraph, identifying a second subgraph applicable to a second work element in the first record, processing the second work element in the second subgraph, receiving a first output work element from the first subgraph, receiving a second output work element from the second subgraph, associating the first output work element with the first record, and associating the second output work element with the first record, in which the first subgraph and the second subgraph are executed at least partially simultaneously.

In general, in one aspect, a data flow is processed in a graph-based computation by identifying a first graph applicable to a first record in the data flow, identifying a first instance of a subgraph as available, processing a first work element in the first record in the first instance of the subgraph, identifying a second instance of the subgraph as available, processing a second work element in the first record in the second instance of the subgraph, receiving a first output work element from the first instance, receiving a second output work element from the second instance, associating the first output work element with the first record, and associating the second output work element with the first record, in which the first instance of the subgraph and the second instance of the subgraph are executed at least partially simultaneously.

Other features and advantages of the invention will be apparent from the description and the claims.

DESCRIPTION

FIGS. 1A and 2 show schematic diagrams of a system for distributed services.

FIG. 1B shows a flow chart of a process for calling a service.

FIGS. 3-5 b show graphs for providing distributed services.

In some examples, there are two ways that graph-based computations interact with distributed services. In some examples, a graph calls a service and relates to the service as if the service were a node in the graph. In other examples, an application relates to a graph such that the graph provides a service. The two cases may be carried out simultaneously. For example, a graph that is providing services to one application may itself call another service to perform part of its computation. The service that one graph calls may be provided by another graph, such that the first graph calls the second rather than containing it as a subgraph, for example.

In some examples, as shown in FIG. 1A, a service node 102 of a graph-based computation 100 operates by calling a service in a distributed service-oriented architecture. That is, when the service node 102 is executed, it calls a service 108, external to the graph 100, as a client of the service. For example, the service node 102 may access a web service using a combination of one or more of SOAP, HTTP, XML, WSDL and the like to communicate with a web service plug-in 104 hosted on an application server 106 to reach the external service 108. The external service 108 may itself be a graph-based computation, as described below. The service may be implemented in a distributed manner using any of a variety of methods for distributing computing tasks.

The service node 102 receives inputs 110 from other graph nodes, e.g., a node 112, in the native format of the graph-based computation 100, such as a record described in a data description language like Ab Initio's DML. The node 102 then formats its inputs 110 into a format appropriate for the type of web service it is using, such as a SOAP- or other type of encapsulation-based request, and transmits a service request 114 to the application server 106. A URL in the request 114 indicates which application server 106, plug in 104, and service 108 to send the request to. The request 114 can be sent in various ways, including by HTTP and RPC (remote procedure call). The plug-in 104 on the server 106 reformats the incoming request 114 as appropriate and transmits an appropriate call 116 to the external service 108.

After the service 108 performs whatever operations it was called for, it returns output 118 to the plug-in 104. The plug-in 104 reformats the output 118 of the service 108 into an appropriate service response 120 to return to the service node 102. The service node 102 reformats the response 120 into an appropriate output format and passes output 121 on to downstream graph nodes, e.g., a node 122. From the point of view of the other graph nodes 112, 122, 124, 126, the distributed service node 102 can participate in the graph computation 100 just as any other node. The structure and organization of the server 106, such as the nature of the plug in 104 and how it communicates to the service 108 (or whether, for example, a plug-in 104 is used at all) do not matter to the graph nodes, as long as the service node 102 receives the type of response 120 it is expecting. The server 106, plug-in 104, and service 108 may themselves be a graph-based computation server, plug-in, and graph, respectively, as described below.

In some examples, the graph 100 is pipelined, meaning (hat each node operates on a separate record, or set of records, of a larger flow of input records in sequential phases of the pipeline. In a given phase, each subsequent node works on a record, or set of records, that it received from a previous node, while each previous node works on the next record or set of records in the input flow. The service node 102 modifies this by accepting additional records and dispatching the work required by each of them to the service 108 before responses corresponding to previous service requests have been received. This is possible where, for example, the service 108 is capable of processing multiple concurrent requests. Records may be composed of separate or separable work elements, which may be handled in pipelined stages as described for records.

If the different requests 114 take different amounts of time to process, the service node 102 may receive the responses in a different order than its inputs 110 were received. This allows multiple services having different processing times to be accessed by a single node 102. A service node such as 102 may optionally be configured to provide its outputs 121 in a different order than its inputs 110 were received. To allow such concurrent processing without using a multi-threaded process for the service node 102 and without including data describing the entire state of the graph 100 in each request 114 (so that it can be used upon its return), the service node 102 keeps track of which requests 114 are outstanding and matches them to incoming responses 120. In some examples, the graph is pipelined such that each record or work element is handled by one node at a time and separate nodes handle separate records or work elements, but multiple records or work elements may be allocated to the single service node 102 at any given time.

In a pipelined graph, the service node 102 treats each record, e.g., each record of a data flow, as an individual transaction. The corresponding request is generated based on the fields in that single input record. Some of the fields may define attributes of the request, such as the URL, while others are passed on as embedded data.

In some examples, as shown in a process 150 in FIG. 1B, the relationships between requests 114 and responses 120 are tracked in a memory accessible by the service node 102. The memory may be a vector, table, file, or other such data structure. When an input record 110 is received 152, a copy of the record 110 is stored 154 to the memory 151. The service node 102 then generates 156 the request 114, sends 158 the request to the service, and writes 160 the ID of the request to the memory, associating it with the stored copy of the record 110. When a response 120 is received 162, it includes an ID corresponding to one of the outstanding requests. The service node 102 matches 164 that ID to an ID in the memory and retrieves 166 the copy of the corresponding input record 110. The record is updated 168 according to the response 120, generating the output 121. If the service node 102 is configured to produce outputs in the order in which they become available (not necessarily the same order as that in which the inputs were received), then the output 121 is provided 170 to the next node 122. If the service node 102 is configured to produce results in the same order as its inputs, the output may be retained 171 in a memory until all outputs corresponding to previous records have been written 121, at which time it can be written 121.

In some examples, a graph-based computation is provided as a network service. We refer to the graph that provides a network service as a provider graph. As shown in FIG. 2, a process 200 running on a client system 202, which may be a graph computation but could be any other type of application, sends a request 204 for a computation requiring the use of a provider graph 206 to an application server 208. The request 204 is sent in a format appropriate for a web-based or other network service, such as a SOAP, HTTP, or RPC request. The request 204 is received in a service interface of the application server 208 according (for SOAP or HTTP requests) to the instructions of a web service plug-in 210 running on the application server 208.

The web services plug-in 210 is an application that processes requests and responses between web services clients and the graphs providing the services. The plug-in 210 is installed and executed on the application server 208. The web services plug-in may communicate with a set of several web services provider graphs. Each of the graphs is included in a services list accessed by the plug-in. The plug-in uses a URL, for example, in a client HTTP request message, to direct the request to a particular graph.

The plug-in 210 formats the service request 204 into an appropriate request 212 for graph processing, such as an RPC request, and forwards this request 212 to a graph server 214 which is hosting the graph 206. In some examples, RPC calls are made straight through from the client 200 to the provider graph 206. At the graph server 214, the inputs 216 included in the request 212 are provided to the graph 206, which is executed to produce outputs 218. The graph server 214 sends the outputs 218 in an appropriate form of graph output 220, such as an RPC response, to the application server 208 and its web service plug-in 210. The plug-in 210 reformats the graph output 220 into a web-services response 222 in a complementary format to the original request 204. The outputs are returned to the client or other entity that called the service in the usual manner of the appropriate network service. The process on the client system 202 then handles the response as it would any other web-based or network service response.

The graph may process inputs using any or all of pipeline parallelism, component parallelism, and data parallelism. That is, multiple sets of inputs may be received, and these may be interleaved in pipeline stages, or they may be processed concurrently by different groups of components, perhaps after partitioning.

In some examples, the public or external interface to a provider graph is defined by a WSDL (Web Services Description Language) file. A WSDL file contains XML-based descriptions of all the messages and operations needed to interact with the provider graph. WSDL files may be defined by organizations, industries, or any other body, for accessing web services relevant to that body. WSDL files may also be customized for a particular implementation. In some examples, a WSDL file is generated from record formats and type definitions of components in subgraphs related to the provider graph. In some examples, a single provider graph includes multiple subgraphs that perform separate services. The WSDL file crated from such subgraphs allows a client to access each of the services provided by the provider graph's subgraphs.

Within the provider graph, the incoming service request input is converted into the internal language of the service-providing subgraph. For example, as shown in FIG. 3, incoming messages 306, such as SOAP requests, include data payloads 308 that require processing by actions specified in the messages. The payloads 308 may be, for example, DML-described records to be processed by graphs within the action processors 300 a, 300 b. An interface layer 301 parses the incoming messages in whatever format they are in and provides appropriate messages to the graph nodes in whatever format they require. On the output side, the interface layer 301 works in the reverse.

Once the message 306 is translated, a Partition by Action Type node 302 interprets the action specified in the message 306 and sends the record 308 in the data payload to the appropriate action processor 300 a or 300 b. In some examples, the partition node 302 does not accept the next incoming message 307 until a gather node 304 informs it that the work for the previous record 308 has been output from the action processor 300 a or 300 b.

In some examples, to achieve concurrency in handling of messages, e.g., service requests, the partition node 302 is configured to partition the action records corresponding to the service requests. This allows multiple requests to be handled simultaneously, if they don't require the same action processor. For example, if a first record 308 requires the action processor 300 a, and a second record 310 requires the action processor 300 b, the partition node 302 sends the second record 310 to the action processor 300 b without waiting for the gather node 304 to report that the first record 308 has completed its processing.

The gather node 304 receives the processed records 308 and 310 from the action processors 300 a and 300 b and packages them into response messages 312 and 313 containing the processed records 308′ or 310′ as data payloads. If the partition node 302 is partitioning transactions, so that multiple records may be processed at once, it is possible that the processed records 308′ or 310′ may be received by the gather node 304 in a different order than their corresponding request messages 306 and 307 were received. An interface 315 between the partition node 302 and gather node 304 allows the gather node to associate the output records 308′, 310′ with the corresponding inputs 308, 310 so that the reply messages 312 and 313 are sent back to the correct client.

The action processors 300 a, 300 b can take any form. As noted above, the records 308, 310 may be in any format, e.g., DML-described. In such a case, the interface layer 301 translates the incoming language, such as SOAP, into the DML format used by the action processors. In some examples, an intermediate language may be used to express the inputs between the interface layer 301 and the partition and gather nodes 302, 304.

In some examples, when gather node 304 is configured to receive records 308′ and 310′ in the order they arrive, processing of actions may be further partitioned within the action processors, as shown for the action processor 300 b in FIG. 3 b. The partition by action type node 302 receives request messages 306 and 307 and routes their records 308 and 310 to the appropriate action processor 300 a or 300 b. Within the action processor 300 b, records are received by another partition node 352 that partitions the records across a pool. Pools refer to multiple instances 350 a, 350 b, 350 c, of a subgraph 350 that carries out the processing of the action processor 300 b. Each instance can process a different record concurrently with the other instances. By partitioning different records 308, 320 to different instances, the action processor 300 b can handle multiple records concurrently, allowing the partition node 302 to send the next record 320 to the action processor before the previous record 308 has come out. A gather node 354 is paired to the partition node 352 so that the nodes can track which instances 350 a, 350 b, 350 c are in use, and use idle instances when possible, increasing concurrency and performance. The gather node 304 still receives the outputs 308′, 310′ of the action processors 300 a, 300 b and supplies the appropriate output messages 312, 313 back to the client through the interface layer 301.

The output records 308′ and 310′ and output messages 312 and 313 may be returned in a different order than the input requests 306 and 307 were received. For example, if the input record 310 contained fewer work elements (described below) than the record 308, then the output response 313 may be returned first even though it corresponds to the later-received request 307. The output order may also vary based on the complexity of the input data, if the action processors 300 a, 300 b work in such a way that records may be processed out of order or at different rates depending on their complexity, or other factors

In some examples, as shown in FIG. 4, the record 406 of a single service request includes data that is processed by the provider graph as a data flow, that is, as multiple work elements 408 a-408 e that move through the graph one after the other. The action processor 300 a (if records aren't further partitioned) or the subgraph 350 (if they are) includes an unwrap node 402 that decomposes the input record 406 into its individual work elements 408 a-408 e and sends the work elements into the service-providing subgraph 400 as appropriate. A corresponding wrapping node 404 recombines the flow of output work elements 408 a′-408 e′ into a single response 412 to return to the client that send the request 406

In some examples, once actions are partitioned and unwrapped into work elements, the work elements are also processed concurrently, as shown in FIG. 5 a. As with the records in FIG. 3 a, a partition node 502 partitions the work elements 408 a-408 e according to their type, that is, which work element processor 500 a or 500 b is required to process each. If the order in which work elements are processed matters, then the partition node 502 sends the work elements 408 to the processors 500 a and 500 b one at a time (possibly partitioning each work element to use both processors), waiting on the gather node 504 to inform it as each output work element 408′ is received. In some examples, the order of processing does not matter, and the partitioning node sends one work element to each processor 500 a and 500 b, assuming the work elements need different processors.

As the work elements 408 a′-408 e′ leave the processors 500 a, 500 b, they are accumulated by the gather node 504. Whenever all the work elements from one record 406 have been collected, they are combined back into the output record 412. Communication 515 between the partition node 502 and the gather node 504 allows the partition node 502 to track which work element processors are available for additional work elements.

Techniques for handling multiple concurrent data flows are also described in U.S. Pat. No. 6,584,581, issued Jun. 24, 2003, and U.S. Pat. No. 6,654,907, issued Nov. 25, 2003, the entire contents of which are hereby incorporated by reference.

FIG. 5 b shows details of how the work element processor 500 b operates and how the subgraph of FIG. 5 a relates to some of the higher-level nodes from FIGS. 3 a, 3 b, and 4, forming a provider graph 501. In some examples, as shown in FIG. 5 b, the work element processor 500 b further partition work elements to be operated on by multiple instances 550 a, 550 b, 550 c of a subgraph 550. An instance 550 a is selected from a pool of available instances of the processes associated with the graph nodes within the subgraph 550. Initially, a work element 408 i is received by the partition node 552. The node 552 has access to the several instances 550 a, 550 b, 550 c of the subgraph 550. The outputs of each instance are received by a gather node 554. The subgraph 550 may in turn call outside processes, such as services or other subgraphs, which may slow the handling of work by those nodes.

In previous systems, a partition component similar to node 552 would have partitioned a single work element 514 into separate components and sent one component to each instance 550 i (where i is a, b, or c). The gather node 554 would have waited until each instance returned an output before combining the outputs into a single output for that work element, eliminating the possibility of concurrency. To maintain the pipeline, the partition node may have sent a delimiter through each instance after the work element so that the gather component would know that the work element was completed when the delimiter was received from each instance. In some examples, the delimiters would be sent after a set of multiple work elements that are handled by a given node in a given phase of the pipeline.

The partition by pool node 552, in contrast, sends the entire work element 408 a to a single instance 550 a for processing. The gather node 554 is configured to expect the output 408 a′ for that work element from only that one instance 550 a. When the expected output 408 a′ is received (in some examples, followed by a delimiter), the gather node 544 provides the output to the next node 504. The gather node 554 does not wait for work clement components or delimiters from the other instances 550 b and 550 c. This allows work element processor 500 b to only use the instances 550 i that are really needed for a given work element. As with the partitioning of transactional records by pool in FIG. 3 b, partitioning work elements by pool also allows multiple work elements to be processed concurrently. Rather than waiting for the first work element 408 a to finish processing and emerge from its instance 550 a, the partition node 552 accepts another work element input 408 c from the upstream node 502 and sends it to the next available instance 550 b. In this way, multiple work elements 408 a, 408 b are processed at the same time, without the partitioning node 552 having to be multithreaded. In some examples, the partitioning node 552 informs the gather node 554, through a communication link 560, about what work elements are in each instance, so that the gather node 554 knows what to expect and can relate the output elements 408 a′, 408 c′ to the input work elements 408 a, 408 c.

Partitioning work elements between pools also allows the outputs 408 a′, 408 b′, 408 c′ to be returned out of order, relative to the inputs 408 a, 408 b, 408 c. For example, if the data in the work element 408 a causes the instance 550 a to take longer process that work element than the instance 550 b takes to process the work element 408 c, the gather node 554 will receive the output 408 c′ before it receives the output 408 a′. Because the two work elements 408 a and 408 c are independent of each other, the gather node 554 can send the output 408 c′ to the next node 504 without waiting for the output 408 a′. This assumes that the other stages (e.g., partion/gather pair 502/504 and unwrap/wrap pair 402/404) are configured to accommodate changes in the order of work elements within the work element processor 500 b. In some examples, it may be necessary or desirable to maintain the order, so the gather node 554 will hold the output 408 c′ until the output 408 a′ is received, and release them to the next node 504 in order. There are still advantages in such a computation, as the instances 550 a, 550 b, 550 c are able to process several work elements simultaneously. This may be useful, for example, if the processing time of the subgraph 550 is longer than that of other nodes or subgraphs.

Partitioning transactions by action type and by pool and partitioning work elements by type and by pool all allow the graph to operate with internal parallelism—that is, with concurrent processing of related or unrelated work elements by different processing elements.

Partitioning by pool, partitioning by type, and other methods of processing requests and their work elements concurrently can be implemented in provider graphs so that the services the graphs provide can themselves handle concurrent inputs. For example, the graph 501 in FIG. 5 b operates as a provider graph when the first node 570 is a subscribe node and the final node 572 is a publish node paired to the subscribe node 570. The subscribe node 570 receives requests 574 including the inputs 406 and forwards the inputs 406 to the partition node 402. The subscribe node 570 coordinates with the publish node 572 to match the outputs 412 with the inputs 406 and package them into appropriate responses 576 (i.e., making sure that each response goes to the client that made the call). Because the graph 501 handles concurrent records and concurrent work elements, the subscribe node 570 can continue accepting input requests 574 without waiting for the publish node 572 to receive the outputs 412. Using subgraphs that partition requests according to action type allows the single provider graph 501 to provide multiple services.

In some examples, the subscribe and publish nodes 570 and 572 assume that individual requests 574 are independent and the order in which responses 576 arc returned does not matter, so the partition and gather nodes 402, 404 in the graph 501 are configured to not enforce order. If order does matter, either or both of the subscribe node 570 and the publish node 572 may make sure that responses 576 are returned the same order as the corresponding inputs 574 were received.

Other implementations are within the scope of the following claims and other claims to which the applicant may be entitled. 

1. A method including: processing a service request according to a computation graph associated with the service by receiving inputs for the computation graph from a service client, providing the inputs to the computation graph as records of a data flow, receiving output from the computation graph, and providing the output to the service client.
 2. The method of claim 1 in which receiving the inputs includes identifying the inputs within a service request from the service client, and providing the inputs to the computation graph includes associating a request identifier with the service request, providing A work clement including the inputs to the computation graph, associating the request identifier with the work element, and communicating the association of the request identifier with the work element to an output node.
 3. The method of claim 2 in which identifying the inputs includes extracting the inputs from an encapsulated request.
 4. The method of claim 3 in which the encapsulated request includes a SOAP request.
 5. The method of claim 1 in which receiving the output includes receiving a work element including outputs from the computation graph, and identifying a request identifier associated with the work element, and providing the output to the service client includes generating a service response corresponding to the service request identified by the request identifier, the service response including the outputs included in the work element.
 6. The method of claim 5 in which generating the service response includes embedding the outputs in an encapsulated response.
 7. The method of claim 6 in which the encapsulated response includes a SOAP response.
 8. The method of claim 1 also including: processing a second service request according to the computation graph by receiving second inputs for the computation graph from a second service client, providing the second inputs to the computation graph as records of a second data flow, receiving second output from the computation graph, and providing the second output to the second service client; and in the computation graph, processing the first inputs and the second inputs concurrently.
 9. The method of claim 8 in which processing the first inputs and the second inputs concurrently includes identifying a first subgraph applicable to the first inputs, processing the first inputs in the first subgraph, identifying a second subgraph applicable to the second inputs, processing the second inputs in the second subgraph, receiving first outputs from the first subgraph, and receiving second outputs from the second subgraph, in which the first subgraph and the second subgraph are executed simultaneously.
 10. The method of claim 8 in which processing the first inputs and the second inputs concurrently includes identifying a first instance of a subgraph as available, processing the first inputs in the first instance of the subgraph, identifying a second instance of the subgraph as available, processing the second inputs in the second instance of the subgraph, receiving first outputs from the first instance, and receiving second outputs from the second instance, in which the first instance of the subgraph and the second instance of the subgraph are executed simultaneously.
 11. A system for processing a service request according to a computation graph associated with the service, the system including: a means for receiving inputs for the computation graph from a service client, a processor configured to provide the inputs to the computation graph as records of a data flow, a means for receiving output from the computation graph, and a means for providing the output to the service client.
 12. A computer program, stored an a computer-readable medium, for processing a service request according to a computation graph associated with the service, the computer program including instructions for causing a computer to: receive inputs for the computation graph from a service client, provide the inputs to the computation graph as records of a data flow, receive output from the computation graph, and provide the output to the service client.
 13. A method of processing a data flow in a graph-based computation including identifying a first graph applicable to a first record in the data flow, identifying a first subgraph applicable to a first work element in the first record, processing the first work element in the first subgraph, identifying a second subgraph applicable to a second work element in the first record, processing the second work element in the second subgraph, receiving a first output work element from the first subgraph, receiving a second output work element from the second subgraph, associating the first output work element with the first record, and associating the second output work element with the first record, in which the first subgraph and the second subgraph are executed at least partially simultaneously.
 14. A method of processing a data flow in a graph-based computation including identifying a first graph applicable to a first record in the data flow, identifying a first instance of a subgraph as available, processing a first work element in the first record in the first instance of the subgraph, identifying a second instance of the subgraph as available, processing a second work element in the first record in the second instance of the subgraph, receiving a first output work element from the first instance, receiving a second output work element from the second instance, associating the first output work element with the first record, and associating the second output work element with the first record, in which the first instance of the subgraph and the second instance of the subgraph are executed at least partially simultaneously.
 15. A system for processing a data flow in a graph-based computation, the system including a means for identifying a first graph applicable to a first record in the data flow, a means for identifying a first subgraph applicable to a first work element in the first record, a means for identifying a second subgraph applicable to a second work element in the first record, first processors configured to process the first work element in the first subgraph and process the second work element in the second subgraph, a means for receiving a first output work element from the first subgraph, a means for receiving a second output work element from the second subgraph, and second processors configured to associate the first output work element with the first record and associate the second output work element with the first record, in which the first processors arc configured to execute the first subgraph and the second subgraph at least partially simultaneously.
 16. A system for processing a data flow in a graph-based computation, the system including a means for identifying a first graph applicable to a first record in the data flow, a means for identifying a first instance of a subgraph as available, a means for identifying a second instance of the subgraph as available, first processors configured to process a first work element in the first record in the first instance of the subgraph and process a second work element in the first record in the second instance of the subgraph, a means for receiving a first output work element from the first instance, a means for receiving a second output work element from the second instance, and second processors configured to associate the first output work element with the first record and associate the second output work element with the first record, in which the first processors are configured to execute the first instance of the subgraph and the second instance of the subgraph at least partially simultaneously.
 17. A computer program, stored on a computer-readable medium, for processing a data flow in a graph-based computation, the computer program including instructions for causing a computer to: identify a first graph applicable to a first record in the data flow, identify a first subgraph applicable to a first work element in the first record, process the first work element, in the first subgraph, identify a second subgraph applicable to a second work element in the first record, process the second work element in the second subgraph, receive a first output work element from the first subgraph, receive a second output work element from the second subgraph, associate the first output work element with the first record, and associate the second output work element with the first record, in which the instructions cause the computer to execute the first subgraph and the second subgraph at least partially simultaneously.
 18. A computer program, stored on a computer-readable medium, for processing a data flow in a graph-based computation, the computer program including instructions for causing a computer to: identify a first graph applicable to a first record in the data flow, identify a first instance of a subgraph as available, process a first work element in the first record in the first instance of the subgraph, identify a second instance of the subgraph as available, process a second work element in the first record in the second instance of the subgraph, receive a first output work element,from the first instance, receive a second output work element from the second instance, associate the first output work element with the first record, and associate the second output work element with the first record, in which the instructions cause the computer to execute the first instance of the subgraph and the second instance of the subgraph at least partially simultaneously. 